The module is available as a YouTube playlist and as a short course with quizzes on Learning Hub (syllabus shown to the right)

YouTube

YouTube

This 6 week short course in the Science of Information Series provides a critical foundation for understanding more advanced science of information topics. Probability theory is essential to conducting quantitative analysis of large sets of data, and in applying to descriptions of complex systems given only partial knowledge of their state.

Estimated time required: 2 hours per week.

Events & Outcomes

Probability Defined

Sample Space & Outcomes

Additional Insights & Examples

Independent Events and Disjointness

Conditional Probability

Independence

Baye's Theorem

Introduction to Random Variables Part 1

Introduction to Random Variables Part 2

Probability Mass Function

Cumulative Distribution Function

Joint Cumulative Distribution Function

Independence of Random Variables

Conditional Mass Function & Random Variables

Finding Expected Value of Random Variable

In depth Example of Finding Expected Value Using Dice

In depth Example of Finding Expected Value Playing Roulette

Calculating Entropy

Example Using a Biased Coin

Example Using Random Words

Discrete Values of Random Variables

Entropy of Random Variables